/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    Resample.java
 *    Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.supervised.instance;

import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Randomizable;
import weka.core.Utils;
import weka.core.WeightedAttributesHandler;
import weka.core.WekaException;
import weka.filters.Filter;
import weka.filters.SupervisedFilter;

/**
 * <!-- globalinfo-start --> Produces a random subsample of a dataset using
 * either sampling with replacement or without replacement.<br/>
 * The original dataset must fit entirely in memory. The number of instances in
 * the generated dataset may be specified. The dataset must have a nominal class
 * attribute. If not, use the unsupervised version. The filter can be made to
 * maintain the class distribution in the subsample, or to bias the class
 * distribution toward a uniform distribution. When used in batch mode (i.e. in
 * the FilteredClassifier), subsequent batches are NOT resampled.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -S &lt;num&gt;
 *  Specify the random number seed (default 1)
 * </pre>
 * 
 * <pre>
 * -Z &lt;num&gt;
 *  The size of the output dataset, as a percentage of
 *  the input dataset (default 100)
 * </pre>
 * 
 * <pre>
 * -B &lt;num&gt;
 *  Bias factor towards uniform class distribution.
 *  0 = distribution in input data -- 1 = uniform distribution.
 *  (default 0)
 * </pre>
 * 
 * <pre>
 * -no-replacement
 *  Disables replacement of instances
 *  (default: with replacement)
 * </pre>
 * 
 * <pre>
 * -V
 *  Inverts the selection - only available with '-no-replacement'.
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Len Trigg (len@reeltwo.com)
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @author Eibe Frank
 * @version $Revision$
 */
public class Resample extends Filter implements SupervisedFilter, OptionHandler, Randomizable, WeightedAttributesHandler {

    /** for serialization. */
    static final long serialVersionUID = 7079064953548300681L;

    /** The subsample size, percent of original set, default 100%. */
    protected double m_SampleSizePercent = 100;

    /** The random number generator seed. */
    protected int m_RandomSeed = 1;

    /** The degree of bias towards uniform (nominal) class distribution. */
    protected double m_BiasToUniformClass = 0;

    /** Whether to perform sampling with replacement or without. */
    protected boolean m_NoReplacement = false;

    /**
     * Whether to invert the selection (only if instances are drawn WITHOUT
     * replacement).
     * 
     * @see #m_NoReplacement
     */
    protected boolean m_InvertSelection = false;

    /**
     * Returns a string describing this filter.
     * 
     * @return a description of the filter suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {
        return "Produces a random subsample of a dataset using either sampling " + "with replacement or without replacement.\n" + "The original dataset must " + "fit entirely in memory. The number of instances in the generated " + "dataset may be specified. The dataset must have a nominal class " + "attribute. If not, use the unsupervised version. The filter can be " + "made to maintain the class distribution in the subsample, or to bias " + "the class distribution toward a uniform distribution. When used in batch " + "mode (i.e. in the FilteredClassifier), subsequent batches are NOT resampled.";
    }

    /**
     * Returns an enumeration describing the available options.
     * 
     * @return an enumeration of all the available options.
     */
    @Override
    public Enumeration<Option> listOptions() {

        Vector<Option> result = new Vector<Option>(5);

        result.addElement(new Option("\tSpecify the random number seed (default 1)", "S", 1, "-S <num>"));

        result.addElement(new Option("\tThe size of the output dataset, as a percentage of\n" + "\tthe input dataset (default 100)", "Z", 1, "-Z <num>"));

        result.addElement(new Option("\tBias factor towards uniform class distribution.\n" + "\t0 = distribution in input data -- 1 = uniform distribution.\n" + "\t(default 0)", "B", 1, "-B <num>"));

        result.addElement(new Option("\tDisables replacement of instances\n" + "\t(default: with replacement)", "no-replacement", 0, "-no-replacement"));

        result.addElement(new Option("\tInverts the selection - only available with '-no-replacement'.", "V", 0, "-V"));

        return result.elements();
    }

    /**
     * Parses a given list of options.
     * <p/>
     * 
     * <!-- options-start --> Valid options are:
     * <p/>
     * 
     * <pre>
     * -S &lt;num&gt;
     *  Specify the random number seed (default 1)
     * </pre>
     * 
     * <pre>
     * -Z &lt;num&gt;
     *  The size of the output dataset, as a percentage of
     *  the input dataset (default 100)
     * </pre>
     * 
     * <pre>
     * -B &lt;num&gt;
     *  Bias factor towards uniform class distribution.
     *  0 = distribution in input data -- 1 = uniform distribution.
     *  (default 0)
     * </pre>
     * 
     * <pre>
     * -no-replacement
     *  Disables replacement of instances
     *  (default: with replacement)
     * </pre>
     * 
     * <pre>
     * -V
     *  Inverts the selection - only available with '-no-replacement'.
     * </pre>
     * 
     * <!-- options-end -->
     * 
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    @Override
    public void setOptions(String[] options) throws Exception {
        String tmpStr;

        tmpStr = Utils.getOption('S', options);
        if (tmpStr.length() != 0) {
            setRandomSeed(Integer.parseInt(tmpStr));
        } else {
            setRandomSeed(1);
        }

        tmpStr = Utils.getOption('B', options);
        if (tmpStr.length() != 0) {
            setBiasToUniformClass(Double.parseDouble(tmpStr));
        } else {
            setBiasToUniformClass(0);
        }

        tmpStr = Utils.getOption('Z', options);
        if (tmpStr.length() != 0) {
            setSampleSizePercent(Double.parseDouble(tmpStr));
        } else {
            setSampleSizePercent(100);
        }

        setNoReplacement(Utils.getFlag("no-replacement", options));

        if (getNoReplacement()) {
            setInvertSelection(Utils.getFlag('V', options));
        }

        if (getInputFormat() != null) {
            setInputFormat(getInputFormat());
        }

        Utils.checkForRemainingOptions(options);
    }

    /**
     * Gets the current settings of the filter.
     * 
     * @return an array of strings suitable for passing to setOptions
     */
    @Override
    public String[] getOptions() {

        Vector<String> result = new Vector<String>();

        result.add("-B");
        result.add("" + getBiasToUniformClass());

        result.add("-S");
        result.add("" + getRandomSeed());

        result.add("-Z");
        result.add("" + getSampleSizePercent());

        if (getNoReplacement()) {
            result.add("-no-replacement");
            if (getInvertSelection()) {
                result.add("-V");
            }
        }

        return result.toArray(new String[result.size()]);
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String biasToUniformClassTipText() {
        return "Whether to use bias towards a uniform class. A value of 0 leaves the class " + "distribution as-is, a value of 1 ensures the class distribution is " + "uniform in the output data.";
    }

    /**
     * Gets the bias towards a uniform class. A value of 0 leaves the class
     * distribution as-is, a value of 1 ensures the class distributions are uniform
     * in the output data.
     * 
     * @return the current bias
     */
    public double getBiasToUniformClass() {
        return m_BiasToUniformClass;
    }

    /**
     * Sets the bias towards a uniform class. A value of 0 leaves the class
     * distribution as-is, a value of 1 ensures the class distributions are uniform
     * in the output data.
     * 
     * @param newBiasToUniformClass the new bias value, between 0 and 1.
     */
    public void setBiasToUniformClass(double newBiasToUniformClass) {
        m_BiasToUniformClass = newBiasToUniformClass;
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String randomSeedTipText() {
        return "Sets the random number seed for subsampling.";
    }

    /**
     * Gets the random number seed.
     * 
     * @return the random number seed.
     */
    public int getRandomSeed() {
        return m_RandomSeed;
    }

    /**
     * Sets the random number seed.
     * 
     * @param newSeed the new random number seed.
     */
    public void setRandomSeed(int newSeed) {
        m_RandomSeed = newSeed;
    }

    public void setSeed(int seed) {
        setRandomSeed(seed);
    }

    public int getSeed() {
        return getRandomSeed();
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String sampleSizePercentTipText() {
        return "The subsample size as a percentage of the original set.";
    }

    /**
     * Gets the subsample size as a percentage of the original set.
     * 
     * @return the subsample size
     */
    public double getSampleSizePercent() {
        return m_SampleSizePercent;
    }

    /**
     * Sets the size of the subsample, as a percentage of the original set.
     * 
     * @param newSampleSizePercent the subsample set size, between 0 and 100.
     */
    public void setSampleSizePercent(double newSampleSizePercent) {
        m_SampleSizePercent = newSampleSizePercent;
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String noReplacementTipText() {
        return "Disables the replacement of instances.";
    }

    /**
     * Gets whether instances are drawn with or without replacement.
     * 
     * @return true if the replacement is disabled
     */
    public boolean getNoReplacement() {
        return m_NoReplacement;
    }

    /**
     * Sets whether instances are drawn with or with out replacement.
     * 
     * @param value if true then the replacement of instances is disabled
     */
    public void setNoReplacement(boolean value) {
        m_NoReplacement = value;
    }

    /**
     * Returns the tip text for this property.
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String invertSelectionTipText() {
        return "Inverts the selection (only if instances are drawn WITHOUT replacement).";
    }

    /**
     * Gets whether selection is inverted (only if instances are drawn WIHTOUT
     * replacement).
     * 
     * @return true if the replacement is disabled
     * @see #m_NoReplacement
     */
    public boolean getInvertSelection() {
        return m_InvertSelection;
    }

    /**
     * Sets whether the selection is inverted (only if instances are drawn WIHTOUT
     * replacement).
     * 
     * @param value if true then selection is inverted
     */
    public void setInvertSelection(boolean value) {
        m_InvertSelection = value;
    }

    /**
     * Returns the Capabilities of this filter.
     * 
     * @return the capabilities of this object
     * @see Capabilities
     */
    @Override
    public Capabilities getCapabilities() {
        Capabilities result = super.getCapabilities();
        result.disableAll();

        // attributes
        result.enableAllAttributes();
        result.enable(Capability.MISSING_VALUES);

        // class
        result.enable(Capability.NOMINAL_CLASS);

        return result;
    }

    /**
     * Sets the format of the input instances.
     * 
     * @param instanceInfo an Instances object containing the input instance
     *                     structure (any instances contained in the object are
     *                     ignored - only the structure is required).
     * @return true if the outputFormat may be collected immediately
     * @throws Exception if the input format can't be set successfully
     */
    @Override
    public boolean setInputFormat(Instances instanceInfo) throws Exception {

        if (getNoReplacement() && getSampleSizePercent() > 100) {
            throw new WekaException("Sample size cannot exceed 100% for sampling *without* replacement!");
        }
        super.setInputFormat(instanceInfo);
        setOutputFormat(instanceInfo);
        return true;
    }

    /**
     * Input an instance for filtering. Filter requires all training instances be
     * read before producing output.
     * 
     * @param instance the input instance
     * @return true if the filtered instance may now be collected with output().
     * @throws IllegalStateException if no input structure has been defined
     */
    @Override
    public boolean input(Instance instance) {

        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }
        if (m_NewBatch) {
            resetQueue();
            m_NewBatch = false;
        }
        if (isFirstBatchDone()) {
            push(instance);
            return true;
        } else {
            bufferInput(instance);
            return false;
        }
    }

    /**
     * Signify that this batch of input to the filter is finished. If the filter
     * requires all instances prior to filtering, output() may now be called to
     * retrieve the filtered instances.
     * 
     * @return true if there are instances pending output
     * @throws IllegalStateException if no input structure has been defined
     */
    @Override
    public boolean batchFinished() {

        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }

        if (!isFirstBatchDone()) {
            // Do the subsample, and clear the input instances.
            createSubsample();
        }
        flushInput();

        m_NewBatch = true;
        m_FirstBatchDone = true;
        return (numPendingOutput() != 0);
    }

    /**
     * Creates a subsample of the current set of input instances. The output
     * instances are pushed onto the output queue for collection.
     */
    protected void createSubsample() {

        Instances data = getInputFormat();

        // Figure out how much data there is per class
        double[] numInstancesPerClass = new double[data.numClasses()];
        for (Instance instance : data) {
            numInstancesPerClass[(int) instance.classValue()]++;
        }

        // Collect data per class
        Instance[][] instancesPerClass = new Instance[data.numClasses()][];
        int numActualClasses = 0;
        for (int i = 0; i < data.numClasses(); i++) {
            instancesPerClass[i] = new Instance[(int) numInstancesPerClass[i]];
            if (numInstancesPerClass[i] > 0) {
                numActualClasses++;
            }
        }
        int[] counterPerClass = new int[data.numClasses()];
        for (Instance instance : data) {
            int classValue = (int) instance.classValue();
            instancesPerClass[classValue][counterPerClass[classValue]++] = instance;
        }

        // Determine how much data we want for each class
        int[] numInstancesToSample = new int[data.numClasses()];
        for (int i = 0; i < data.numClasses(); i++) {

            // Can't sample any data if there is no data for the class
            if (numInstancesPerClass[i] == 0) {
                continue;
            }

            // Blend observed prior and uniform prior based on user-defined blending
            // parameter
            int sampleSize = (int) ((m_SampleSizePercent / 100.0) * ((1 - m_BiasToUniformClass) * numInstancesPerClass[i] + m_BiasToUniformClass * data.numInstances() / numActualClasses));
            if (getNoReplacement() && sampleSize > numInstancesPerClass[i]) {
                System.err.println("WARNING: Not enough instances of " + data.classAttribute().value(i) + " for selected value of bias parameter in supervised Resample filter when sampling without replacement.");
                sampleSize = (int) numInstancesPerClass[i];
            }
            numInstancesToSample[i] = (int) sampleSize;
        }

        // Sample data
        Random random = new Random(m_RandomSeed);
        if (!getNoReplacement()) {
            for (int i = 0; i < data.numClasses(); i++) {
                int numEligible = (int) numInstancesPerClass[i];
                for (int j = 0; j < numInstancesToSample[i]; j++) {
                    // Sampling with replacement
                    push(instancesPerClass[i][random.nextInt(numEligible)]);
                }
            }
        } else {
            for (int i = 0; i < data.numClasses(); i++) {
                int numEligible = (int) numInstancesPerClass[i];

                // Set up array of indices
                int[] selected = new int[numEligible];
                for (int j = 0; j < numEligible; j++) {
                    selected[j] = j;
                }
                for (int j = 0; j < numInstancesToSample[i]; j++) {

                    // Sampling without replacement
                    int chosenLocation = random.nextInt(numEligible);
                    int chosen = selected[chosenLocation];
                    numEligible--;
                    selected[chosenLocation] = selected[numEligible];
                    selected[numEligible] = chosen;
                }

                // Do we need to invert the selection?
                if (getInvertSelection()) {

                    // Take the first numEligible instances
                    // because they have not been selected
                    for (int j = 0; j < numEligible; j++) {
                        push(instancesPerClass[i][selected[j]]);
                    }
                } else {

                    // Take the elements that have been selected
                    for (int j = numEligible; j < (int) numInstancesPerClass[i]; j++) {
                        push(instancesPerClass[i][selected[j]]);
                    }
                }
            }
        }
    }

    /**
     * Main method for testing this class.
     * 
     * @param argv should contain arguments to the filter: use -h for help
     */
    public static void main(String[] argv) {
        runFilter(new Resample(), argv);
    }
}
